Lead Engineer, Pricing & Decision Systems

Job Description

Job Description

Company Overview:

One Park Financial (OPF) is a leading Financial Technology company dedicated to empowering small businesses by connecting them with flexible financing and funding options. Our mission is to provide entrepreneurs with the working capital they need to elevate their businesses. At OPF, we believe in working with high-performing individuals who are ready to play an integral part in our company's expansion — because our success hinges on our people.

Why Join Us?

At OPF, we foster a dynamic and inclusive company culture that emphasizes collaboration, innovation, and personal growth. Our team is composed of passionate, driven individuals who are committed to making a difference. Here’s what you can expect when you join our team:

• Innovative Environment: Work with cutting-edge technology and be part of a team that is constantly pushing the boundaries of fintech.
• Professional Growth: We invest in our employees’ growth with continuous learning opportunities, training programs, and career advancement paths.
• Supportive Culture: Enjoy a supportive and inclusive work environment where your ideas are valued, and your contributions make a real impact.
• Community Focus: Be part of a company that understands the importance of small and mid-sized businesses to their communities and the nation’s financial health.
• High-Performing Team: Join a team of badasses who are committed to excellence and are integral to our company's expansion and success.

About the Role

We are looking for a Lead Engineer to own and evolve the Python pricing engine that prices every new business and renewal offer we make. This system decides the terms we put in front of small businesses in real time, so when it prices correctly we grow profitably, and when it prices wrong, even by a single reference file, it costs us real money at scale. You will be the primary developer and technical steward of the engine: building new capabilities, keeping it reliable, and making sure every change is correct before it ships. You will lead one or more engineers who support the system, setting the technical direction and review standards they work to.

This is a hands-on lead role, not a hands-off manager role. You will still be in the code every week, and you will also be the person who breaks down the work, reviews the risky changes, and keeps the team shipping accurately and on time. You will coordinate across Product, DevOps, Data Engineering, Data Science, and QA to take pricing changes from a specification to production with confidence.

The pricing engine is where you start, not where you stop. This is the first dedicated engineering hire on our analytics team, and the person in it becomes the technical foundation for how we build analytics products going forward. Once you have the engine running reliably, the role grows into integrating our machine-learning models into production, building the internal analytics applications and data products other teams rely on, and owning the data plumbing and quality checks those applications depend on. We are looking for someone who wants to build that platform with us, not only maintain one system.

We are an AI-forward company, and we expect our engineers to work that way. We want someone who leans on modern AI and LLM tooling to build, test, and ship faster and sharper, not someone who does things the slow, manual way.

We want to work with high-performing people who will play an integral part in transforming the company. We understand one thing: it all comes down to working with creative and committed people and enabling them to do what they do best.

Responsibilities

• Serve as the primary technical owner of our Python-based pricing and decision engine, deployed on AWS (FastAPI, ECS/Fargate, DynamoDB, Lambda, SQS, Secrets Manager, CloudWatch).
• Implement and integrate new scoring models into the pricing flow, working from Data Science specifications, and maintain the offer grids, modifiers, and rules that set terms, rates, origination fees, and offer sizing.
• Own the pricing logic end to end, from scoring through offer construction, and ensure the business logic is correct before it ships. Treat a mispriced offer as the serious incident it is.
• Lead and mentor the engineers supporting the system. Set architecture, coding, and review standards, own code review for anything that touches pricing, and spec the risky changes clearly so the team ships accurately.
• Own the correctness of the data the engine stores in DynamoDB and S3, including per-decision audit records, experiment history, and the versioned reference files that drive pricing. Build the reconciliation and quality checks that catch problems before they reach production, not weeks later in an analysis.
• Design, run, and analyze A/B tests on pricing strategies, including who is eligible, how variants are assigned and balanced, and the integrity of the experiment data you later measure against.
• Own the integration with the sales platform the engine prices for, and the data flow to and from the CRM and external credit and bank-data providers.
• Keep the engine reliable and observable, and maintain the health dashboard the team uses to monitor it in production.
• Partner with Product on the pricing roadmap and the pricing UI, with DevOps on deployment and incident response, with Data Engineering on the upstream data and pipelines the engine depends on, with Data Science on model deployment, and with QA on test strategy and coverage.
• Communicate system behavior and changes clearly to non-technical stakeholders, and keep these teams aligned on shared services, API contracts, and deployment workflows.
• Use modern AI and LLM tooling to speed up your own work, from prototyping to documentation to testing.

Where this role grows (beyond the pricing engine):

As the first dedicated engineering hire on the analytics team, you will build out the broader analytics platform once the pricing engine is in steady hands:

• Integrate machine-learning models built by our data science and marketing analytics teams into production, writing the feature and scoring logic that puts a model's output to work in the pricing flow and other decisions, and partnering with Data Science on iteration.
• Build the internal analytics applications and data products other teams depend on, from automated reporting to decision-support tools.
• Own the data storage, plumbing, and quality checks those analytics applications and models depend on.
• Set the engineering standards and technical direction for analytics development as the team grows around you.

Requirements

• 5+ years of professional Python development, including building and maintaining production applications and services, not just scripts or notebooks.
• Experience leading or mentoring engineers: setting standards, reviewing others' code, and directing a small team's work.
• Experience building and maintaining REST APIs (FastAPI, Flask, or similar).
• Hands-on AWS experience operating production services, particularly ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, and CloudWatch.
• Real care for data correctness in production storage: designing DynamoDB read and write access patterns, handling schema changes and type boundaries cleanly, and managing versioned data in S3 such as Parquet and partitioned datasets. You should be able to talk about a time a data-storage bug bit you and how you would prevent it.
• Strong SQL, and comfort working with relational and NoSQL data and reference files such as CSV and Parquet.
• Comfort with business-logic-heavy applications where correctness matters, including reading and correctly implementing rules from scorecards, reference files, and configuration data.
• Familiarity with data modeling and validation frameworks such as Pydantic, and the instinct to build reconciliation and QA checks rather than trusting writes blindly.
• Hands-on experience integrating machine learning or analytical model outputs into production systems, including feature engineering and scoring logic, and collaborating with Data Science on model deployment and iteration.
• Experience designing or supporting A/B tests and experimentation, including an appreciation for stratification and clean experiment data.
• Strong communication and cross-team collaboration, able to work across Product, DevOps, Data Engineering, Data Science, and QA, each with a different technical context, and keep them aligned.
• Self-directed, and comfortable owning a system end to end.
• Comfort using modern AI and LLM tools to make your own work more efficient.

Nice to have:

• Background in financial services, lending, or pricing systems.
• Experience with CRM or platform APIs such as Salesforce or HubSpot.
• CI/CD and containerized deployments (Docker, ECS).
• Analytics-engineering tooling such as dbt or Airflow.
• Front-end familiarity (Next.js, TypeScript) for the admin and monitoring dashboard.
• Experience building AI infrastructure and agents (RAG, LangChain, LangGraph, Agents SDK).
• Exposure to ML model serving and deployment tooling (model monitoring, retraining pipelines, MLflow or SageMaker). Not required for day one, but an area this role grows into as we build out the analytics platform

Benefits

• Local & National Health Insurance
• Dental and Vision insurance
• Group Medical Bridge
• 401k with Match
• ID Protection: 100% covered by the company
• Life Insurance: 100% covered by the company
• Generous PTO and holidays